Deep neural networks (DNN) have achieved dramatic success in many areas, including computer vision, natural language processing, and recommendation systems. Their success largely depends upon the availability of a wealthy amount of training data. In many real-world applications, however, training data is typically distributed across different organizations, which are unwilling to share their data because of privacy and regulation concerns directly. To alleviate these concerns, federated learning (FL) [1]} is proposed to enable multiple clients to collaboratively build a machine learning model without sharing clients' private data.
